--- language: zh datasets: CLUECorpusSmall widget: - text: "北京是[MASK]国的首都。" --- # Chinese Xlarge Whole Word Masking RoBERTa Model ## Model description This is an xlarge Chinese Whole Word Masking RoBERTa model pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits [UER-py](https://github.com/dbiir/UER-py/) to support models with parameters above one billion, and extends it to a multimodal pre-training framework. In order to facilitate users in reproducing the results, we used a publicly available corpus and word segmentation tool, and provided all training details. You can download the model either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the link [roberta-xlarge-wwm-chinese-cluecorpussmall](https://huggingface.co/uer/roberta-xlarge-wwm-chinese-cluecorpussmall): ## How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='uer/roberta-xlarge-wwm-chinese-cluecorpussmall') >>> unmasker("北京是[MASK]国的首都。") [ {'score': 0.9298505783081055, 'token': 704, 'token_str': '中', 'sequence': '北 京 是 中 国 的 首 都 。'}, {'score': 0.05041525512933731, 'token': 2769, 'token_str': '我', 'sequence': '北 京 是 我 国 的 首 都 。'}, {'score': 0.004921116400510073, 'token': 4862, 'token_str': '祖', 'sequence': '北 京 是 祖 国 的 首 都 。'}, {'score': 0.0020684923510998487, 'token': 3696, 'token_str': '民', 'sequence': '北 京 是 民 国 的 首 都 。'}, {'score': 0.0018144999630749226, 'token': 3926, 'token_str': '清', 'sequence': '北 京 是 清 国 的 首 都 。'} ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('uer/roberta-xlarge-wwm-chinese-cluecorpussmall') model = BertModel.from_pretrained("uer/roberta-xlarge-wwm-chinese-cluecorpussmall") text = "用你喜欢的任何文本替换我。" encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('uer/roberta-xlarge-wwm-chinese-cluecorpussmall') model = TFBertModel.from_pretrained("uer/roberta-xlarge-wwm-chinese-cluecorpussmall") text = "用你喜欢的任何文本替换我。" encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. ## Training procedure Models are pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 500,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. [jieba](https://github.com/fxsjy/jieba) is used as word segmentation tool. Stage1: ``` python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ --vocab_path models/google_zh_vocab.txt \ --dataset_path cluecorpussmall_seq128_dataset.pt \ --processes_num 32 --seq_length 128 \ --dynamic_masking --data_processor mlm ``` ``` deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.json --dataset_path cluecorpussmall_seq128_dataset.pt \ --vocab_path models/google_zh_vocab.txt \ --config_path models/bert/xlarge_config.json \ --output_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq128_model \ --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ --total_steps 500000 --save_checkpoint_steps 50000 --report_steps 500 \ --learning_rate 2e-5 --batch_size 128 --deep_init \ --whole_word_masking --deepspeed_checkpoint_activations \ --data_processor mlm --target mlm ``` Before stage2, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints: ``` python3 models/cluecorpussmall_wwm_roberta_xlarge_seq128_model/zero_to_fp32.py models/cluecorpussmall_wwm_roberta_xlarge_seq128_model/ \ models/cluecorpussmall_wwm_roberta_xlarge_seq128_model.bin ``` Stage2: ``` python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ --vocab_path models/google_zh_vocab.txt \ --dataset_path cluecorpussmall_seq512_dataset.pt \ --processes_num 32 --seq_length 512 \ --dynamic_masking --data_processor mlm ``` ``` deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.json --dataset_path cluecorpussmall_seq512_dataset.pt \ --vocab_path models/google_zh_vocab.txt \ --config_path models/bert/xlarge_config.json \ --pretrained_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq128_model.bin \ --output_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq512_model \ --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 500 \ --learning_rate 5e-5 --batch_size 32 \ --whole_word_masking --deepspeed_checkpoint_activations \ --data_processor mlm --target mlm ``` Then, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints: ``` python3 models/cluecorpussmall_wwm_roberta_xlarge_seq512_model/zero_to_fp32.py models/cluecorpussmall_wwm_roberta_xlarge_seq512_model/ \ models/cluecorpussmall_wwm_roberta_xlarge_seq512_model.bin ``` Finally, we convert the pre-trained model into Huggingface's format: ``` python3 scripts/convert_bert_from_tencentpretrain_to_huggingface.py --input_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq512_model.bin \ --output_model_path pytorch_model.bin \ --layers_num 36 --type mlm ``` ### BibTeX entry and citation info ``` @article{zhao2019uer, title={UER: An Open-Source Toolkit for Pre-training Models}, author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong}, journal={EMNLP-IJCNLP 2019}, pages={241}, year={2019} } @article{zhao2023tencentpretrain, title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities}, author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others}, journal={ACL 2023}, pages={217}, year={2023} } ```